@inproceedings{6e83c8be63ba49caa749d9fb4bcbedf5,
title = "Privacy-Preserving and Reliable Federated Learning",
abstract = "In Internet of Things (IoT), it is often impossible to share datasets owned by different participants (usually IoT devices) for machine learning model training due to privacy concerns. Federated learning (FL) is a promising technique to address this challenge. However, existing FL schemes face the problem of how to avoid low-quality/malicious update. To solve this problem, we propose a privacy-preserving and reliable federated learning scheme (PPRFLS) to select reliable participants and evaluate the quality of the participants{\textquoteright} updates. Analysis shows that the proposed scheme achieves data privacy and model reliability.",
keywords = "Data privacy, Federated learning, Model reliability",
author = "Yi Lu and Lei Zhang and Lulu Wang and Yuanyuan Gao",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 21st International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2021 ; Conference date: 03-12-2021 Through 05-12-2021",
year = "2022",
doi = "10.1007/978-3-030-95391-1\_22",
language = "英语",
isbn = "9783030953904",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "346--361",
editor = "Yongxuan Lai and Tian Wang and Min Jiang and Guangquan Xu and Wei Liang and Aniello Castiglione",
booktitle = "Algorithms and Architectures for Parallel Processing - 21st International Conference, ICA3PP 2021, Proceedings",
address = "德国",
}